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All Journal AGRISAINS Indonesia Medicus Veterinus Jurnal Teknologi dan Manajemen Informatika JOIN (Jurnal Online Informatika) Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis JOIV : International Journal on Informatics Visualization Madania: Jurnal Ilmu-Ilmu Keislaman Jurnal SOLMA JURNAL ILMIAH PETERNAKAN TERPADU Technomedia Journal TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Enrichment : Journal of Management Community Development Journal: Jurnal Pengabdian Masyarakat Portofolio: Jurnal Ekonomi, Bisnis, Manajemen dan Akuntansi KEYNESIA : INTERNATIONAL JOURNAL OF ECONOMY AND BUSINESS Neo Journal of economy and social humanities Intellect : Indonesian Journal of Learning and Technological Innovation Coreid Journal Jurnal Abdimas International Journal of Active Learning Khazanah Multidisiplin
Mohamad Irfan, Mohamad
Mahasiswa Jurusan Teknik Mesin, Fakultas Teknik, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Veterinary Other

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Journal : coreid journal

 1 
Academic Data Quality Measurement in SALAM Application Using Six Sigma Method Firdaus, Imam; Alam, Cecep Nurul; Gerhana, Yana Aditia; Irfan, Mohamad; Iskandar, Ibrahim
CoreID Journal Vol. 3 No. 2 (2025): July 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i2.136

Abstract

Data quality plays a critical role in ensuring the reliability and usefulness of information for decision making in higher education institutions. However, academic data within the SALAM application at UIN Sunan Gunung Djati Bandung has not previously undergone a systematic quality assessment, leading to uncertainty in several managerial and academic decisions. This study aims to evaluate the quality of academic data in the SALAM application using the Six Sigma method with the DMAIC (Define–Measure–Analyze–Improve–Control) framework. Five data quality dimensions completeness, consistency, conformity, uniqueness, and timeliness are employed to measure and analyze data quality performance. The measurement process begins with data definition and extraction, followed by quantitative analysis using sigma metrics. The results indicate that the overall quality of academic data is at a moderate level, with an average sigma score of approximately 3, primarily influenced by incomplete and inconsistent data. In contrast, the timeliness dimension demonstrates excellent performance, achieving a sigma metric of 6 due to the long-term availability of data over more than ten years. This study contributes by providing an empirical, data-driven evaluation of academic data quality and offers practical insights for implementing continuous monitoring and improvement strategies to enhance data reliability and support more effective decision making in higher education institutions.
Implementation of Template Matching Algorithm in Detecting Student Identification Numbers to Improve Student Services Cahya, Nurul Dwi; Irfan, Mohamad; Amin, Mohammad Badrudin
CoreID Journal Vol. 3 No. 2 (2025): July 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i2.141

Abstract

The rapid progression of technological advancements, particularly in the digitalization of image data, has significantly facilitated numerous sophisticated applications, including pattern recognition. A prominent example can be observed within the education system of UIN Sunan Gunung Djati Bandung, where the Student Identification Number (NIM) constitutes a pivotal component in a wide range of academic service operations. At present, processes such as the verification of scholarship documentation, updating of PD DIKTI data, and the borrowing of library materials are predominantly executed through manual means, frequently resulting in operational inefficiencies and the occurrence of human errors. To address these challenges, this study investigates the application of the template matching algorithm for recognizing the NIM on the Student Identity Card (KTM). This study is conducted to systematically evaluate the implementation of template matching for NIM recognition, assess the performance of the proposed method, and ascertain its impact on enhancing student services. The experimental findings reveal that the template matching algorithm demonstrates variable success rates across three trials (9/20, 8/20, and 8/20 instances correctly identified). The detection accuracy is determined to be influenced by factors including, but not limited to, template values, the presence of noise, variations in lighting conditions, and the parameter settings of the Canny edge detection process. The results substantiate the potential of the template matching algorithm to significantly improve the efficiency of student services by automating the NIM recognition process. Nonetheless, several technical limitations, particularly those impacting detection accuracy, necessitate further refinement to optimize its performance. This research highlights the critical importance of enhancing the algorithm to establish a robust and effective system for academic service delivery.
Comparison of Classification Models for Predicting Admission Outcomes of Prospective Students with Disabilities Anwar, Rosihon; Irfan, Mohamad; Nurjaman, Ilham
CoreID Journal Vol. 4 No. 1 (2026): March 2026
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v4i1.147

Abstract

Students with disabilities are a group that requires special attention in the admission process at universities, especially at State Islamic Higher Education Institutions (PTKIN). Although inclusive policies have been implemented, challenges in implementation in the field are still quite significant, especially in terms of equal access and the readiness of educational institutions. This study aims to analyze the opportunities and challenges of accepting students with disabilities at PTKIN through a machine learning approach to predict the factors that influence selection graduation. The research data consists of 80 prospective students with disabilities who participated in the PTKIN selection, covering variables such as gender, province of origin, previous education, school accreditation, and type of disability. The research process included data cleaning, feature engineering (including categorical encoding and recategorization of disability variables), and data balancing using the SMOTE method. Next, model training was carried out using three main algorithms, namely Support Vector Machine (SVM), Random Forest, and XGBoost, as well as model combination (ensemble voting classifier) for performance comparison. The results show that the SVM (RBF kernel) model provides the best performance with an accuracy of 80% and an F1-score of 0.88 for the “Pass” class. This model outperforms Random Forest and XGBoost, which have an accuracy of 65% each. The most influential factors for graduation are the province of origin, disability category, and previous form of education. These findings indicate that the acceptance of students with disabilities at PTKIN is still influenced by geographical factors and educational background, so affirmative policies need to be directed at expanding access for people with disabilities from certain regions and backgrounds. The machine learning approach has proven to be effective as a tool for analyzing inclusive education policies in the PTKIN environment.
Co-Authors Abdurohim Abdurohim, Abdurohim Aldy Rialdy Atmadja Amam, Amam Amin, Mohammad Badrudin Amiruddin Dg. Malewa Andi Hiroyuki Andri Zarman, Andri Andriyan, Acep Razif Angelyna, Angelyna Anggreni, Ni Gusti Ayu Beki Subaeki, Fatkhan Gunawan, Aldy Rialdy Atmadja, Beki Budianda, Sherly Indria Cahya, Nurul Dwi Cecep Nurul Alam, Cecep Nurul Chaidir, John Dahlan, Maharani M. Deny Wiria Nugraha Desmiza, Desmiza Diningrat, Sriwulan Cakrawati Dwi Agustian Dwi Wijaya, Kadek Agus Eka Yulianti Eko Supraptono Elok Rosyidah Esi Fitriani Komara, Esi Fitriani Fadila, Nurul Ferikawita M. Sembiring Firdaus, Imam Ghea Revina Wigantini Haerofiatna Hajra Rasmita Ngemba Hamid, Odai Amer Hidayattullah, Ardhana Luthfi Humolungo, Nurlela H. Ian Septiana, Ian Ilham Nurjaman, Ilham Iskandar, Ibrahim Jumadi Jumadi Juwita Meldasari Tebisi, Juwita Meldasari Katrina Feby Lestari Khumaedi, Muhammmad Kurniawan, Galang Wahid Lustinasari, Kholisah Mardani Mardani Maskunatin, Iva Maulita, Miftahul Maylawati, Maylawati Mohammad Ismail Muhammad Hamdani, Muhammad Mustafa Mutrofin Mutrofin Nirwana, Nirwana Nouval Trezandy Lapatta Nur Lukman NurRohman, Alfian Paramita, Veronika Santi Raivandy, I Made Randhy Ramli, Rosmini Rizka Ardiansyah Rosihon Anwar, Rosihon Said Sunardiyo Sanjaya, Komang Sa’adillah, Dian Simatupang, Frido Saritua Sintiawati, Ni Wayan Sirajuddin Abdullah Sonhaji Sonhaji Sudiari, Ni Made Dwi Susyani, Novi Syahrir Syahrir Syahrullah Syahrullah Syaiful Hendra Tri Lestari, Sinta Tukiman Tukiman Undang Syaripudin, Undang Vidya Urbaningrum Urbaningrum Walili, Marzelina Wildan Budiawan Zulfikar Wisnu Uriawan, Wisnu Yana Aditia Gerhana, Yana Aditia Yeri Sutopo Yudi Mujayin Zainal Zainal